MATH-520 / 5 crédits

Enseignant: Chizat Lénaïc

Langue: Anglais


Summary

Mathematical analysis of modern supervised machine learning techniques, from linear methods to artificial neural networks.

Content

Keywords

Supervised learning, Machine learning, Neural networks, Optimization, Statistics

Learning Prerequisites

Required courses

Analysis, Linear Algebra, Probability and Statistics

Important concepts to start the course

  • A good knowledge of undergraduate mathematics is important.

  • Ability to code in a scientific computing programming language of your choice (e.g. Python, Matlab, Julia). The course will involve coding exercises and assignments.

  • Having followed an introductory class on machine learning is beneficial.

 

Learning Outcomes

  • Contextualise the research literature on theoretical machine learning
  • Interpret the practical behavior of complex machine learning pipelines through the lens of mathematical theory
  • Implement simple supervised learning algorithms from scratch
  • Reason on how statistical and optimization phenomena interact in machine learning practice
  • Distinguish between what is known and what is not known in the theory of deep learning

Assessment methods

Homeworks, projects, presentation

Dans les plans d'études

  • Semestre: Automne
  • Forme de l'examen: Pendant le semestre (session d'hiver)
  • Matière examinée: Mathematics of machine learning
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Pendant le semestre (session d'hiver)
  • Matière examinée: Mathematics of machine learning
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Pendant le semestre (session d'hiver)
  • Matière examinée: Mathematics of machine learning
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Pendant le semestre (session d'hiver)
  • Matière examinée: Mathematics of machine learning
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines

Semaine de référence

 LuMaMeJeVe
8-9    MAA110
9-10    
10-11    MAA110
11-12    
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22     

Vendredi, 8h - 10h: Cours MAA110

Vendredi, 10h - 12h: Exercice, TP MAA110